Introduction In Machine Learning, one essential step is evaluating the performance of a model. For classification models, the Confusion Matrix serves as a fundamental instrument for evaluating the performance. The Confusion Matrix provides a visualization of the results of a model. Based on the information from the Confusion Matrix, some...
Introduction In Machine Learning, one essential step is evaluating the performance of a model. For classification models, the Confusion Matrix serves as a fundamental instrument for evaluating the performance. It provides a clear and visual summary of the prediction accuracy of a model by illustrating the correspondence between the predicted...
Introduction In this tutorial, we want to scale features of a Pandas DataFrame. In order to do this, we use the StandardScaler() class and the MinMaxScaler() class of Scikit-Learn. Import Libraries First, we import the following python modules: import pandas as pd from sklearn.datasets import fetch_california_housing from...
Introduction Machine Learning can be divided into two main types: Supervised Learning and Unsupervised Learning. In this tutorial, we want to take a closer look to these approaches and compare them to each other. Overview Both supervised learning and unsupervised learning have their own characteristics and are suitable for solving...
Introduction In this tutorial, we want to take a closer look to the most important Machine Learning algorithms and compare them to each other. Overview...
Introduction In this tutorial, we want to create an Artificial Neural Network (ANN) that is able to recognize the species of penguins based on their body measurements. We will cover the entire process from Data Collection, Data Cleansing and Feature Engineering to Model Creation and Model Training. Use Case Based...